Issue #189
February 5, 2023
Dear friends,
Welcome to the 189th edition of the Sunday Briefing. We were offline over the past few weeks due to a death in the family but we’re now back to our regular schedule.
We’re on hiatus from blogging, but you’re welcome to catch up on our latest Medium post “Top 10 Books we read in 2022”, the latest post on the G4Sci series: Network Attacks: Breaking up a network without observing it completely or the latest Viz4Sci in the series: Photo Color Scheme.
Several of you have reached out in recent weeks asking how they can help support the work we do here at Data For Science. While the Sunday Briefing is a labor of love and will always remain free, it is not without costs, and any help is appreciated.
If you wish to support our work, there are several options:
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On our regularly scheduled content we learn how to use Vectorization to say goodbye to loops in Python, how to build a scraping tool for Linkedin in 7 minutes and some remarks on Large Language Models.
On the academic front we dive into the Technology of Decentralized Finance (DeFi), ChatGPT: five priorities for research and how Higher-Order Patterns Reveal Causal Timescales of Complex System.
This week’s Data Science Book is “Practical Linear Algebra for Data Science” by M. X. Cohen. As always you can find all the previous book recommendations on our website. In the video of the week, we have “Python for linear algebra (for absolute beginners)”
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Semper discentes,
The D4S Team
This week’s Data Science Book is " Practical Linear Algebra for Data Science " by M. X. Cohen, a book written for self-studying learners who need to learn how to apply linear algebra in their work. The book is self-contained and can be used as a standalone resource, but it can also be used as a supplement to a lecture-based course. Whether you are trying to enhance your understanding of linear algebra or learn the subject from scratch, this is a valuable resource that provides a clear and practical approach to the subject.
The author is an excellent instructor that recognizes that traditional linear algebra textbooks can be frustrating for those looking to use the subject as a tool for understanding data, statistics, deep learning, image processing, and other technical fields. Instead of memorizing equations and abstract proofs, the author provides clear explanations and practical examples to help the reader understand how to think about matrices, vectors, and operations. The focus of the book is to help the reader develop a visual, geometric, intuition for linear algebra and how to implement these concepts in Python code, particularly for applications in machine learning and data science.
- 1. Vectorization: say goodbye to loops in Python [medium.com/@matteo.bernard]
- 2. Finally, a Fast Algorithm for Shortest Paths on Negative Graphs [quantamagazine.org]
- 3. New AI classifier for indicating AI-written text [openai.com]
- 4. Some remarks on Large Language Models [gist.github.com/yoavg]
- 5. Building Effective Ratio Features for Machine Learning Models [soliduslabs.com]
- 6. John Carmack's 'Different Path' to Artificial General Intelligence [dallasinnovates.com]
- 7. How to build a scraping tool for Linkedin in 7 minutes [blog.devgenius.io]
- • The Technology of Decentralized Finance (DeFi) (R. Auer, B. Haslhofer, S. Kitzler, P. Saggese, F. Victor)
- • Activity networks determine project performance (A. Vazquez, I. Pozzana, G. Kalogridis, C. Ellinas)
- • Learning from data with structured missingness (R. Mitra, S. F. McGough, T. Chakraborti, C. Holmes, R. Copping, N. Hagenbuch, S. Biedermann, J. Noonan et al)
- • ChatGPT: five priorities for research (E. A. M. van Dis, J. Bollen, W. Zuidema, R. van Rooij, C. L. Bockting)
- • Infrastructure adaptation and emergence of loops in network routing with time-dependent loads (A. Lonardi, E. Facca, M. Putti, C. De Bacco)
- • Exact and rapid linear clustering of networks with dynamic programming (A. Patania, A. Allard, J.-G. Young)
- • Impact of the Euro 2020 championship on the spread of COVID-19 (J. Dehning, S. B. Mohr, S. Contreras, P. Dönges, E. N. Iftekhar, O. Schulz, P. Bechtle, V. Priesemann)
- • Higher-Order Patterns Reveal Causal Timescales of Complex Systems (L. V. Petrović, A. Wegner, I. Scholtes)
Python for linear algebra (for absolute beginners)
All our videos are also available in our
YouTube playlist.
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